Contents

Solar Plants

Solar Plants

Data Import 

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
from folium import plugins
import folium


pd.set_option('display.float_format', lambda x: '%0.4f' % x)

/Users/jnapolitano/venvs/finance/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

US Power Plants Data 

gisfilepath = "/Users/jnapolitano/Projects/gas.jnapolitano.io/source/data/Power_Plants.geojson"

powerplants_df = gpd.read_file(gisfilepath)
#Selecting only Operational Plants
powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()
na = powerplants_df.COAL_USED.min()
powerplants_df.replace(na, 0 , inplace=True)

#powerplants_df = powerplants_df.to_crs(epsg=3857)

powerplants_df.describe()
OBJECTID LATITUDE LONGITUDE OPER_CAP SUMMER_CAP WINTER_CAP PLAN_CAP RETIRE_CAP GEN_UNITS PLAN_UNIT RETIR_UNIT COAL_USED NGAS_USED OIL_USED NET_GEN CAP_FACTOR LINES SOURCE_LAT SOURC_LONG
count 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000 8736.0000
mean 5709.4960 39.0405 -95.0667 134.5691 123.2273 127.9356 1.5068 12.3725 2.3368 0.0144 0.2098 75285.8993 1351867.2459 3590.2573 466814.5072 0.0371 0.3489 39.0424 -94.1651
std 3315.5893 5.5191 19.0667 367.0281 339.7523 350.9757 36.1032 100.9833 2.8505 0.1797 0.9739 555638.6353 6289781.4216 57026.9476 1813781.4037 12.5042 0.5880 5.5178 21.7038
min 1.0000 18.9737 -171.7124 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -3465212.0000 -1141.5514 0.0000 18.9742 -171.7124
25% 2666.7500 35.1990 -113.7045 2.7000 2.5000 2.4000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 2141.7500 0.0574 0.0000 35.1986 -113.6161
50% 5832.5000 39.4456 -91.6117 9.6000 8.7500 8.8000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 11727.0000 0.2137 0.0000 39.4492 -91.0609
75% 8566.2500 42.4995 -78.6045 74.8000 70.0000 72.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 133402.5000 0.4291 1.0000 42.5003 -78.2997
max 11810.0000 71.2921 -67.4016 6809.0000 7079.0000 7079.0000 2200.0000 3623.0000 73.0000 5.0000 28.0000 10040760.0000 138014506.0000 4130642.0000 32377477.0000 4.6214 11.0000 71.2920 36.2439

Identifying the Solar Plants in the Dataset 

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
solar_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'SUN'].copy()

#concatenating coal type dfs into one
solar_plants.describe()
OBJECTID LATITUDE LONGITUDE OPER_CAP SUMMER_CAP WINTER_CAP PLAN_CAP RETIRE_CAP GEN_UNITS PLAN_UNIT RETIR_UNIT COAL_USED NGAS_USED OIL_USED NET_GEN CAP_FACTOR LINES SOURCE_LAT SOURC_LONG
count 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000 2834.0000
mean 7157.9675 37.7763 -92.9739 10.4763 10.2730 10.0978 0.3627 0.1403 1.1401 0.0056 0.0106 0.0000 1134.2445 0.0000 16101.9108 -0.1025 0.0212 37.7813 -91.8098
std 3684.7414 4.1140 19.5182 30.1760 29.9266 29.7078 11.3019 5.5945 1.0293 0.0919 0.1546 0.0000 18862.3855 0.0000 62440.4517 3.9574 0.1464 4.1213 22.5932
min 116.0000 20.7667 -159.5817 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -2611.0000 -114.1551 0.0000 20.7667 -159.5817
25% 3190.5000 34.8295 -116.5268 1.5000 1.5000 1.4000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1004.2500 0.0528 0.0000 34.8240 -116.4832
50% 9028.5000 36.7623 -83.1576 3.0000 3.0000 2.8500 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3275.5000 0.1667 0.0000 36.7784 -82.1681
75% 10131.7500 41.3015 -76.3683 5.0000 5.0000 5.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 8676.0000 0.2081 0.0000 41.3208 -76.1351
max 11810.0000 47.5475 -69.6717 585.9000 585.9000 585.9000 400.0000 290.0000 16.0000 3.0000 6.0000 0.0000 462108.0000 0.0000 1265760.0000 4.6214 2.0000 47.5475 33.6561

Solar Plants Interactive Map 

solar_map = solar_plants.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
solar_map
Make this Notebook Trusted to load map: File -> Trust Notebook

Solar Plant Density Interactive Map 

solar_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in solar_plants.geometry ]
#

solar_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

solar_heat_layer = plugins.HeatMap(data = solar_heat_data, show=True)
solar_heat_layer.add_to(solar_density_map)

solar_density_map
Make this Notebook Trusted to load map: File -> Trust Notebook

previous

Wind Plants

next

Hydro Plants